Limits...
Detailed protein sequence alignment based on Spectral Similarity Score (SSS).

Gupta K, Thomas D, Vidya SV, Venkatesh KV, Ramakumar S - BMC Bioinformatics (2005)

Bottom Line: Detailed comparison established close similarities between subsequences that do not have any significant character identity.The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures.The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in

ABSTRACT

Background: The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.

Results: Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of similarity in structure.

Conclusion: An algorithm is developed which is inspired by successful application of spectral similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

Show MeSH
3D matching for PAKd PAKe using SPDBV magic fit. 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0A. Color red is used for PKCd and yellow for PKCe. The subsequences in the figures are (a)MKEALSTE & DDSRIGQT (b) ANQPFCAV & QTFLLDPY (c) GKAEFWLD & ANCTIQFE (d) QAKVLMSV & RVYVIIDL (e) RVIQIVLM & RKIELAVF belonging to PKCd and PKCe respectively. All subsequences are completely dissimilar using character based approaches but are found to be similar using SSS. Appreciably low rms values confirms that the subsequences in fig 4a-4e are similar subsequences.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC1131888&req=5

Figure 4: 3D matching for PAKd PAKe using SPDBV magic fit. 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0A. Color red is used for PKCd and yellow for PKCe. The subsequences in the figures are (a)MKEALSTE & DDSRIGQT (b) ANQPFCAV & QTFLLDPY (c) GKAEFWLD & ANCTIQFE (d) QAKVLMSV & RVYVIIDL (e) RVIQIVLM & RKIELAVF belonging to PKCd and PKCe respectively. All subsequences are completely dissimilar using character based approaches but are found to be similar using SSS. Appreciably low rms values confirms that the subsequences in fig 4a-4e are similar subsequences.

Mentions: PKCd and PKCe, though share a similar fold, do not superimpose well using SPDBV but our experiments suggest that the subsequences picked up by SSS within the threshold do produce good fits with low rms (root mean square) value apart from their similarity in the secondary structure (also shown in the table 4). Figure 4 shows the fits obtained using SPDBV for subsequences that were picked by the algorithm with the exception of Figure 6 which reported a high SSS value, and also has reported a high rms value during pdb fitting. Matches found with high character identity are not shown in the table, but in general their SSS value is lower which is taken care of by the threshold. This demonstrates that the algorithm's ability to pick non identical subsequences if they are similar in their tertiary structure. The accounting of subsequences through SSS that are found below threshold would increase the BLAST similarity score by more than 10% in this particular example and more than 5% in most other protein pairs. However, the potency of the algorithm essentially remains in capturing "interesting" subsequences and not perse at global alignment.


Detailed protein sequence alignment based on Spectral Similarity Score (SSS).

Gupta K, Thomas D, Vidya SV, Venkatesh KV, Ramakumar S - BMC Bioinformatics (2005)

3D matching for PAKd PAKe using SPDBV magic fit. 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0A. Color red is used for PKCd and yellow for PKCe. The subsequences in the figures are (a)MKEALSTE & DDSRIGQT (b) ANQPFCAV & QTFLLDPY (c) GKAEFWLD & ANCTIQFE (d) QAKVLMSV & RVYVIIDL (e) RVIQIVLM & RKIELAVF belonging to PKCd and PKCe respectively. All subsequences are completely dissimilar using character based approaches but are found to be similar using SSS. Appreciably low rms values confirms that the subsequences in fig 4a-4e are similar subsequences.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1131888&req=5

Figure 4: 3D matching for PAKd PAKe using SPDBV magic fit. 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0A. Color red is used for PKCd and yellow for PKCe. The subsequences in the figures are (a)MKEALSTE & DDSRIGQT (b) ANQPFCAV & QTFLLDPY (c) GKAEFWLD & ANCTIQFE (d) QAKVLMSV & RVYVIIDL (e) RVIQIVLM & RKIELAVF belonging to PKCd and PKCe respectively. All subsequences are completely dissimilar using character based approaches but are found to be similar using SSS. Appreciably low rms values confirms that the subsequences in fig 4a-4e are similar subsequences.
Mentions: PKCd and PKCe, though share a similar fold, do not superimpose well using SPDBV but our experiments suggest that the subsequences picked up by SSS within the threshold do produce good fits with low rms (root mean square) value apart from their similarity in the secondary structure (also shown in the table 4). Figure 4 shows the fits obtained using SPDBV for subsequences that were picked by the algorithm with the exception of Figure 6 which reported a high SSS value, and also has reported a high rms value during pdb fitting. Matches found with high character identity are not shown in the table, but in general their SSS value is lower which is taken care of by the threshold. This demonstrates that the algorithm's ability to pick non identical subsequences if they are similar in their tertiary structure. The accounting of subsequences through SSS that are found below threshold would increase the BLAST similarity score by more than 10% in this particular example and more than 5% in most other protein pairs. However, the potency of the algorithm essentially remains in capturing "interesting" subsequences and not perse at global alignment.

Bottom Line: Detailed comparison established close similarities between subsequences that do not have any significant character identity.The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures.The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in

ABSTRACT

Background: The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.

Results: Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of similarity in structure.

Conclusion: An algorithm is developed which is inspired by successful application of spectral similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

Show MeSH